newline Rainfall prediction is a multidimensional, dynamic, data-intensive and chaotic process. It is one of the several computational tasks identified by meteorologists as the core problem in weather modeling across the globe. This investigation aims to determine the significant atmospheric parameters and appropriate predictive techniques to enhance the precision of weather prediction system using hybrid data-driven approach. The remarkable contribution of this thesis is to improve the efficiency of the data-driven approach by exploiting the empirical characteristics of the input weather parameters. The research outcomes reveal that, the fundamental and generic data mining techniques are ineffective to comprehend the hidden input-output relationship. Therefore, this research
newlineintroduces an optimal feature selection using Maximum Frequency Weighted Reduct
newlineSelection (MFWRS) and Reduct Selection Using Genetic Algorithm (RSGA) to identify
newlinethe effective input parameters in modeling short-term rainfall forecast scenario.
newlineThe thesis further outlines the application of different intelligent computing
newlineapproaches based on rough sets, fuzzy sets, evolutionary computing, neural networks and
newlinetheir implications for the practical workings of short-term rainfall forecasting. The
newlineproposed hybrid frameworks modeled using Adaptive Neuro-Fuzzy Inference Systems,
newlineFuzzy Rule-Based Classification and contemporary data mining techniques performed
newlinesubstantially better when trained with feature subsets generated using the proposed
newlineoptimal reduct selection techniques. The thesis introduces an unconventional Adaptive
newlineRough-Evolutionary Neuro Approach (ARENA) and Adaptive Rough Neuro-Fuzzy
newlineApproach (ARNFA) based hybrid intelligent systems. The proposed ARENA achieved
newline98.01% prediction accuracy with a nominal error rate of 1.99%. The methodical analysis
newlinerevealed ARENA as an appropriate tool to deal with the real-world rainfall prediction
newlineefficiently by means of identifying significant weather parameters.